






"""simple docstring"""
import argparse
import json
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


snake_case : Optional[int]     		=		1_6
snake_case : Dict     		=		3_2


def 							A						(				__snake_case: Accelerator ,     __snake_case: int = 1_6 ,     __snake_case: str = "bert-base-cased"  )    ->		List[Any]:

       """simple docstring"""





       __magic_name__      				=    AutoTokenizer.from_pretrained(__snake_case  )
       __magic_name__      				=    load_dataset('glue' ,     'mrpc'  )

       def tokenize_function(__snake_case: Union[str, Any]  ):
              # max_length=None => use the model max length (it's actually the default)
              __magic_name__      				=    tokenizer(examples['sentence1'] ,     examples['sentence2'] ,     truncation=__snake_case ,     max_length=__snake_case  )
              return outputs

       # Apply the method we just defined to all the examples in all the splits of the dataset
       __magic_name__      				=    datasets.map(
           __snake_case ,     batched=__snake_case ,     remove_columns=['idx', 'sentence1', 'sentence2'] ,     load_from_cache_file=__snake_case  )

       # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
       # transformers library
       __magic_name__      				=    tokenized_datasets.rename_column('label' ,     'labels'  )

       def collate_fn(__snake_case: Dict  ):
              # On TPU it's best to pad everything to the same length or training will be very slow.
              if accelerator.distributed_type == DistributedType.TPU:
                     return tokenizer.pad(__snake_case ,     padding='max_length' ,     max_length=1_2_8 ,     return_tensors='pt'  )
              return tokenizer.pad(__snake_case ,     padding='longest' ,     return_tensors='pt'  )

       # Instantiate dataloaders.
       __magic_name__      				=    DataLoader(
           tokenized_datasets['train'] ,     shuffle=__snake_case ,     collate_fn=__snake_case ,     batch_size=__snake_case  )
       __magic_name__      				=    DataLoader(
           tokenized_datasets['validation'] ,     shuffle=__snake_case ,     collate_fn=__snake_case ,     batch_size=__snake_case  )

       return train_dataloader, eval_dataloader


def 							A						(				__snake_case: Optional[Any] ,     __snake_case: Any ,     __snake_case: Optional[int] ,     __snake_case: Dict  )    ->		Optional[Any]:

       """simple docstring"""





       model.eval()
       __magic_name__      				=    0
       for step, batch in enumerate(__snake_case  ):
              # We could avoid this line since we set the accelerator with `device_placement=True`.
              batch.to(accelerator.device  )
              with torch.no_grad():
                     __magic_name__      				=    model(**__snake_case  )
              __magic_name__      				=    outputs.logits.argmax(dim=-1  )
              # It is slightly faster to call this once, than multiple times
              __magic_name__				,  __magic_name__      				=    accelerator.gather(
                  (predictions, batch['labels'])  )  # If we are in a multiprocess environment, the last batch has duplicates
              if accelerator.use_distributed:
                     if step == len(__snake_case  ) - 1:
                            __magic_name__      				=    predictions[: len(eval_dataloader.dataset  ) - samples_seen]
                            __magic_name__      				=    references[: len(eval_dataloader.dataset  ) - samples_seen]
                     else:
                            samples_seen += references.shape[0]
              metric.add_batch(
                  predictions=__snake_case ,     references=__snake_case ,     )

       __magic_name__      				=    metric.compute()
       return eval_metric["accuracy"]


def 							A						(				__snake_case: Tuple ,     __snake_case: Any  )    ->		Dict:

       """simple docstring"""





       __magic_name__      				=    Accelerator()

       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       __magic_name__      				=    config['lr']
       __magic_name__      				=    int(config['num_epochs']  )
       __magic_name__      				=    int(config['seed']  )
       __magic_name__      				=    int(config['batch_size']  )
       __magic_name__      				=    args.model_name_or_path

       set_seed(__snake_case  )
       __magic_name__				,  __magic_name__      				=    get_dataloaders(__snake_case ,     __snake_case ,     __snake_case  )

       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       __magic_name__      				=    AutoModelForSequenceClassification.from_pretrained(__snake_case ,     return_dict=__snake_case  )

       # Instantiate optimizer
       __magic_name__      				=    (
           AdamW
           if accelerator.state.deepspeed_plugin is None
           or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
           else DummyOptim
       )
       __magic_name__      				=    optimizer_cls(params=model.parameters() ,     lr=__snake_case  )

       if accelerator.state.deepspeed_plugin is not None:
              __magic_name__      				=    accelerator.state.deepspeed_plugin.deepspeed_config[
                  'gradient_accumulation_steps'
              ]
       else:
              __magic_name__      				=    1
       __magic_name__      				=    (len(__snake_case  ) * num_epochs) // gradient_accumulation_steps

       # Instantiate scheduler
       if (
           accelerator.state.deepspeed_plugin is None
           or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
       ):
              __magic_name__      				=    get_linear_schedule_with_warmup(
                  optimizer=__snake_case ,     num_warmup_steps=0 ,     num_training_steps=__snake_case ,     )
       else:
              __magic_name__      				=    DummyScheduler(__snake_case ,     total_num_steps=__snake_case ,     warmup_num_steps=0  )

       # Prepare everything
       # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
       # prepare method.
       __magic_name__				,  __magic_name__				,  __magic_name__				,  __magic_name__				,  __magic_name__      				=    accelerator.prepare(
           __snake_case ,     __snake_case ,     __snake_case ,     __snake_case ,     __snake_case  )

       # We need to keep track of how many total steps we have iterated over
       __magic_name__      				=    0
       # We also need to keep track of the stating epoch so files are named properly
       __magic_name__      				=    0
       __magic_name__      				=    evaluate.load('glue' ,     'mrpc'  )
       __magic_name__      				=    num_epochs

       if args.partial_train_epoch is not None:
              __magic_name__      				=    args.partial_train_epoch

       if args.resume_from_checkpoint:
              accelerator.load_state(args.resume_from_checkpoint  )
              __magic_name__      				=    args.resume_from_checkpoint.split('epoch_'  )[1]
              __magic_name__      				=    ''
              for char in epoch_string:
                     if char.isdigit():
                            state_epoch_num += char
                     else:
                            break
              __magic_name__      				=    int(__snake_case  ) + 1
              __magic_name__      				=    evaluation_loop(__snake_case ,     __snake_case ,     __snake_case ,     __snake_case  )
              accelerator.print('resumed checkpoint performance:' ,     __snake_case  )
              accelerator.print('resumed checkpoint\'s scheduler\'s lr:' ,     lr_scheduler.get_lr()[0]  )
              accelerator.print('resumed optimizers\'s lr:' ,     optimizer.param_groups[0]['lr']  )
              with open(os.path.join(args.output_dir ,     F"""state_{starting_epoch-1}.json"""  ) ,     'r'  ) as f:
                     __magic_name__      				=    json.load(__snake_case  )
                     assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
                     assert (
                         resumed_state["lr"] == lr_scheduler.get_lr()[0]
                     ), "Scheduler learning rate mismatch, loading from checkpoint failed"
                     assert (
                         resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
                     ), "Optimizer learning rate mismatch, loading from checkpoint failed"
                     assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
                     return

    # Now we train the model
       __magic_name__      				=    {}
       for epoch in range(__snake_case ,     __snake_case  ):
              model.train()
              for step, batch in enumerate(__snake_case  ):
                     __magic_name__      				=    model(**__snake_case  )
                     __magic_name__      				=    outputs.loss
                     __magic_name__      				=    loss / gradient_accumulation_steps
                     accelerator.backward(__snake_case  )
                     if step % gradient_accumulation_steps == 0:
                            optimizer.step()
                            lr_scheduler.step()
                            optimizer.zero_grad()

                     overall_step += 1
              __magic_name__      				=    F"""epoch_{epoch}"""
              __magic_name__      				=    os.path.join(args.output_dir ,     __snake_case  )
              accelerator.save_state(__snake_case  )
              __magic_name__      				=    evaluation_loop(__snake_case ,     __snake_case ,     __snake_case ,     __snake_case  )
              __magic_name__      				=    accuracy
              __magic_name__      				=    lr_scheduler.get_lr()[0]
              __magic_name__      				=    optimizer.param_groups[0]['lr']
              __magic_name__      				=    epoch
              __magic_name__      				=    overall_step
              accelerator.print(F"""epoch {epoch}:""" ,     __snake_case  )

              accelerator.wait_for_everyone()
              if accelerator.is_main_process:
                     with open(os.path.join(args.output_dir ,     F"""state_{epoch}.json"""  ) ,     'w'  ) as f:
                            json.dump(__snake_case ,     __snake_case  )


def 							A						(				)    ->		Union[str, Any]:

       """simple docstring"""





       __magic_name__      				=    argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.'  )
       parser.add_argument(
           '--model_name_or_path' ,     type=__snake_case ,     default='bert-base-cased' ,     help='Path to pretrained model or model identifier from huggingface.co/models.' ,     required=__snake_case ,     )
       parser.add_argument(
           '--output_dir' ,     type=__snake_case ,     default='.' ,     help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.' ,     )
       parser.add_argument(
           '--resume_from_checkpoint' ,     type=__snake_case ,     default=__snake_case ,     help='If the training should continue from a checkpoint folder.' ,     )
       parser.add_argument(
           '--partial_train_epoch' ,     type=__snake_case ,     default=__snake_case ,     help='If passed, the training will stop after this number of epochs.' ,     )
       parser.add_argument(
           '--num_epochs' ,     type=__snake_case ,     default=2 ,     help='Number of train epochs.' ,     )
       __magic_name__      				=    parser.parse_args()
       __magic_name__      				=    {'lr': 2E-5, 'num_epochs': args.num_epochs, 'seed': 4_2, 'batch_size': 1_6}

       training_function(__snake_case ,     __snake_case  )


if __name__ == "__main__":
 main()







"""simple docstring"""
def 							A						(				__snake_case: int  )    ->		int:

       """simple docstring"""





       if divisor % 5 == 0 or divisor % 2 == 0:
              return 0
       __magic_name__      				=    1
       __magic_name__      				=    1
       while repunit:
              __magic_name__      				=    (1_0 * repunit + 1) % divisor
              repunit_index += 1
       return repunit_index


def 							A						(				__snake_case: int = 1_0_0_0_0_0_0  )    ->		int:

       """simple docstring"""





       __magic_name__      				=    limit - 1
       if divisor % 2 == 0:
              divisor += 1
       while least_divisible_repunit(__snake_case  ) <= limit:
              divisor += 2
       return divisor


if __name__ == "__main__":
 print(f"""{solution() = }""")